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Douglass-SAS-DIMACS
Computer Science Living-Learning Community for Women




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The Douglass-SAS-DIMACS Computer Science Living-Learning Community (CS LLC) is an opportunity available to first-year undergraduate women at Rutgers who have indicated a strong interest in majoring in Computer Science. Participating students enjoy the benefits of sharing common residential and academic experiences while making new friends, exploring common interests, and being part of a close community of peers.

The CS LLC creates an immersive educational environment that promotes success in the major by providing:

  • Housing together on the Rutgers Busch Campus.

  • A first-year seminar course on “Great Ideas and Applications in Computer Science.”

  • Multi-layered mentoring that includes a graduate mentor, an undergraduate peer leader, a faculty advisor, and dedicated Douglass Project staff members.

  • Community-building programs and events to promote student-faculty engagement.

  • Research experiences in CS, study groups, and CS-oriented community service.

  • Opportunities to explore educational themes outside the classroom by integrating interaction with faculty and industry.

CS LLC participants are expected to engage in the community by:

  • Attending living-learning community sponsored activities.

  • Enrolling in the 1-credit “Great Ideas and Applications in Computer Science.” that explores innovative and real-world applications of computer science and related ideas.

  • Enrolling in the 4-credit “Introduction to Computer Science” (CS 111) course that is required of CS majors.

  • Enrolling in the 3-credit Douglass College course “Knowledge & Power: Issues in Women’s Leadership” that examines challenges and opportunities confronting women in today’s society. This course is taken by all students in Douglass Residential College.

To be eligible you should:

  • Be an incoming first-year student.

  • Join Douglass Residential College.

  • Have intent to major in Computer Science.

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This material is based in part upon work supported by the National Science Foundation under award DUE-1504775. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.